Field
Implementations described herein generally relate to analyzing time series traces to detect excursions in sensors employed in one or more semiconductor processing chambers.
Description of the Related Art
Manufacturing silicon substrates involves a series of operations, which may include a lithography operation. In the lithography operation, a mask pattern is imaged onto a substrate that is at least partially covered by a layer of radiation-sensitive material (resist). Prior to this imaging operation, the substrate may undergo various procedures, such as priming, resist coating and a soft bake. After exposure, the substrate may be subjected to other procedures, such as a post-exposure bake (PEB), development, a hard bake and measurement/inspection of the imaged features. This array of procedures is used as a basis to pattern an individual layer of a device, e.g., an IC. Such a patterned layer may then undergo various processes such as etching, ion-implantation (doping), metallization, oxidation, chemo-mechanical polishing, etc., all intended to finish off an individual layer. If several layers are required, then the whole procedure, or a variant thereof, is repeated for each new layer. Eventually, an array of devices will be present on the substrate (substrate). These devices are then separated from one another by a technique such as dicing or sawing, whence the individual devices can be mounted on a carrier, connected to pins, etc.
Usually a number of different processing operations may be performed in a single processing system or “tool” which includes a plurality of processing chambers. During processing, each chamber in which a procedure is carried out may include a plurality of sensors, with each sensor configured to monitor a predefined metric relating to substrate processing.
Further, these multiple silicon substrate processing operations occur over an interval of time. A process may include a transition from a first operation to a second operation. Time-series data is data collected over the interval of time, including the transition (e.g., the time-series transition). Typically, statistical methods (e.g., statistical process control (SPC)) are utilized to analyze sensor data for semiconductor manufacturing processes. However, SPC and other statistical methods of monitoring processes are not capable of monitoring time-series transitions to detect excursions (e.g., outliers in the time trace data). Outlier (excursion) detection in sensor trace data aids in assessing the overall health of the chamber in which a substrate processing procedure is carried forth. Outlier detection needs to increasingly become more sensitive to detect anomalies in a more refined range of sensor data, especially when batches of substrates increase to the thousands.
Statistical methods cannot detect short-time signal perturbations in data received from sensors over time. Statistical methods also provide false positives (e.g., that an entire signal does not match a target signal because a minimal portion of the signal is outside of a guard band) and do not allow for adjustment of the sensitivity of outlier detection.
Therefore, there is a continual need for an improved method of detecting outliers/excursions in sensor data retrieved during semiconductor processing.